def results_for_preview(data_name=""):
from Orange.data import Table
from Orange.evaluation import CrossValidation
from Orange.classification import \
LogisticRegressionLearner, SVMLearner, NuSVMLearner
data = Table(data_name or "heart_disease")
results = CrossValidation(data, [
LogisticRegressionLearner(penalty="l2"),
LogisticRegressionLearner(penalty="l1"),
SVMLearner(probability=True),
NuSVMLearner(probability=True)
],
store_data=True)
results.learner_names = ["LR l2", "LR l1", "SVM", "Nu SVM"]
return results
def results_for_preview(data_name=""):
from Orange.data import Table
from Orange.evaluation import CrossValidation
from Orange.classification import \
LogisticRegressionLearner, SVMLearner, NuSVMLearner
data = Table(data_name or "ionosphere")
results = CrossValidation(
data,
[LogisticRegressionLearner(penalty="l2"),
LogisticRegressionLearner(penalty="l1"),
SVMLearner(probability=True),
NuSVMLearner(probability=True)
],
store_data=True
)
results.learner_names = ["LR l2", "LR l1", "SVM", "Nu SVM"]
return results
def test_report_widgets_evaluate(self):
rep = OWReport.get_instance()
data = Table("zoo")
widgets = self.eval_widgets
results = CrossValidation(data, [LogisticRegressionLearner()], store_data=True)
results.learner_names = ["LR l2"]
w = OWTestLearners()
set_learner = getattr(w, w.inputs[0].handler)
set_train = getattr(w, w.inputs[1].handler)
set_test = getattr(w, w.inputs[2].handler)
set_learner(LogisticRegressionLearner(), 0)
set_train(data)
set_test(data)
w.create_report_html()
rep.make_report(w)
self.assertEqual(len(widgets) + 1, 4)
self._create_report(widgets, rep, results)
def test_report_widgets_evaluate(self):
rep = OWReport.get_instance()
data = Table("zoo")
widgets = self.eval_widgets
results = CrossValidation(data, [LogisticRegressionLearner()],
store_data=True)
results.learner_names = ["LR l2"]
w = self.create_widget(OWTestLearners)
set_learner = getattr(w, w.Inputs.learner.handler)
set_train = getattr(w, w.Inputs.train_data.handler)
set_test = getattr(w, w.Inputs.test_data.handler)
set_learner(LogisticRegressionLearner(), 0)
set_train(data)
set_test(data)
w.create_report_html()
rep.make_report(w)
self._create_report(widgets, rep, results)
def test_report_widgets_evaluate(self):
rep = OWReport.get_instance()
data = Table("zoo")
widgets = self.eval_widgets
results = CrossValidation(data,
[LogisticRegressionLearner()],
store_data=True,
k=3)
results.learner_names = ["LR l2"]
w = self.create_widget(OWTestLearners)
set_learner = getattr(w, w.Inputs.learner.handler)
set_train = getattr(w, w.Inputs.train_data.handler)
set_test = getattr(w, w.Inputs.test_data.handler)
set_learner(LogisticRegressionLearner(), 0)
set_train(data)
set_test(data)
w.create_report_html()
rep.make_report(w)
self._create_report(widgets, rep, results)
def test_report_widgets_evaluate(self):
app = QApplication(sys.argv)
rep = OWReport.get_instance()
data = Table("zoo")
widgets = self.eval_widgets
results = CrossValidation(data, [LogisticRegressionLearner()],
store_data=True)
results.learner_names = ["LR l2"]
w = OWTestLearners()
set_learner = getattr(w, w.inputs[0].handler)
set_train = getattr(w, w.inputs[1].handler)
set_test = getattr(w, w.inputs[2].handler)
set_learner(LogisticRegressionLearner(), 0)
set_train(data)
set_test(data)
w.create_report_html()
rep.make_report(w)
self.assertEqual(len(widgets) + 1, 4)
self._create_report(widgets, rep, results, app)
